TY - GEN
T1 - Time and Frequency Domain Feature Selection Using Mutual Information for EEG-based Emotion Recognition
AU - Wibawa, Adhi Dharma
AU - Fatih, Nur
AU - Pamungkas, Yuri
AU - Pratiwi, Monica
AU - Ramadhani, Prio Adi
AU - Suwadi,
N1 - Publisher Copyright:
© 2022 Institute of Advanced Engineering and Science (IAES).
PY - 2022
Y1 - 2022
N2 - Emotion Recognition using EEG signals remains a challenging task. Usually, feature extraction and channel selection are determined based on neuro-scientific assumptions. Too many features during the EEG-based human emotion recognition will lead to reduced classification accuracy and consume high computational costs. This study analyzes time and frequency domain features such as Mean, Mean Absolute Value, Standard Deviation, and Power Spectral Density. In this study, an EEG Recording session involved 25 subjects consisting of 12 males and 13 females. Video with two emotions, happy and sad, were stimulated to the subjects. The electrodes were placed in channels F7, F8, FP1, and FP2 based on the 10/20 EEG system. The EEG pre-processing, such as signal filtering, Automatic Artifact Removal EOG, Artifact Subspace Reconstruction, and Independent Component Analysis, were done using MATLAB Toolbox, followed by Infinite Impulse Response with Butterworth was applied to separate the EEG signal into alpha, beta, and gamma sub-band. Therefore, 48 numbers of features were extracted to perform emotion recognition. Mutual Information is used for calculating the degree of importance of each feature. Then, the features were ranked to eliminate features with a minimal contribution. We implemented a Random Forest algorithm to classify human emotions based on the EEG signal. The experimental results show that reducing the number of utilized features from 48 to 12 can increase the accuracy score from 82.61 % to 95.65 %.
AB - Emotion Recognition using EEG signals remains a challenging task. Usually, feature extraction and channel selection are determined based on neuro-scientific assumptions. Too many features during the EEG-based human emotion recognition will lead to reduced classification accuracy and consume high computational costs. This study analyzes time and frequency domain features such as Mean, Mean Absolute Value, Standard Deviation, and Power Spectral Density. In this study, an EEG Recording session involved 25 subjects consisting of 12 males and 13 females. Video with two emotions, happy and sad, were stimulated to the subjects. The electrodes were placed in channels F7, F8, FP1, and FP2 based on the 10/20 EEG system. The EEG pre-processing, such as signal filtering, Automatic Artifact Removal EOG, Artifact Subspace Reconstruction, and Independent Component Analysis, were done using MATLAB Toolbox, followed by Infinite Impulse Response with Butterworth was applied to separate the EEG signal into alpha, beta, and gamma sub-band. Therefore, 48 numbers of features were extracted to perform emotion recognition. Mutual Information is used for calculating the degree of importance of each feature. Then, the features were ranked to eliminate features with a minimal contribution. We implemented a Random Forest algorithm to classify human emotions based on the EEG signal. The experimental results show that reducing the number of utilized features from 48 to 12 can increase the accuracy score from 82.61 % to 95.65 %.
KW - EEG
KW - Emotion Recognition
KW - Feature Selection
KW - Mutual Information
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85142700245&partnerID=8YFLogxK
U2 - 10.23919/EECSI56542.2022.9946522
DO - 10.23919/EECSI56542.2022.9946522
M3 - Conference contribution
AN - SCOPUS:85142700245
T3 - International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
SP - 19
EP - 24
BT - Proceedings - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
A2 - Facta, Mochammad
A2 - Syafrullah, Mohammad
A2 - Riyadi, Munawar Agus
A2 - Subroto, Imam Much Ibnu
A2 - Irawan, Irawan
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
Y2 - 6 October 2022 through 7 October 2022
ER -